1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
20/12/2024 Plata basureros 10000 Tami NA
3/12/2024 Agua 15828 Andrés PAC AGUAS ANDIN 000000005687837
22/12/2024 Comida 46608 Tami Supermercado
10/12/2024 Otros 47484 Andrés viaje brasil
29/12/2024 Electricidad 27417 Andrés NA
29/12/2024 Comida 83284 Tami Supermercado
30/12/2024 Comida 30000 Andrés nueces almendras mix etc
30/12/2024 Otros 47484 Andrés viaje a brasil (duplicar para q cargue sobre tami)
4/1/2025 Diosi 53999 Andrés n y d pumpkin 7.5
4/1/2025 Comida 15260 Andrés NA
6/1/2025 Comida 40988 Tami Supermercado
8/1/2025 Pago cámaras MB 20000 Tami NA
13/1/2025 Comida 67387 Tami Supermercado
20/1/2025 Comida 21692 Andrés gnoccis
20/1/2025 Comida 86884 Tami Supermercado
21/1/2025 Comida 21525 Andrés piwen
23/1/2025 VTR 21990 Andrés NA
25/1/2025 Diosi 20000 Andrés arena diosi
27/1/2025 Comida 71516 Tami Supermercado
30/1/2025 Electricidad 55000 Andrés NA
6/2/2025 Comida 52730 Andrés supermercado (no cobre el otro de 25k pq muchas son cosas mías)
9/2/2025 Comida 12500 Andrés NA
17/2/2025 Comida 7940 Andrés NA
18/2/2025 Electricidad 64888 Andrés la puse por adelantado para que no se me olvide
18/2/2025 Comida 17820 Tami Supermercado
23/2/2025 Comida 86908 Tami Supermercado
27/2/2025 Comida 10000 Andrés NA
26/2/2025 Comida 4620 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 9.7913e+08   2    4.9311 0.0074 ** 
## lag_depvar    2.6171e+11   1 2636.0276 <2e-16 ***
## Residuals     8.0716e+10 813                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1922.597 16380.27 0.1527337
## 2-0 31477.066 23235.055 39719.08 0.0000000
## 2-1 24248.228 19469.603 29026.85 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
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## 29   28706.00             0   28640.00
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## 707  95424.29             2   99637.71
## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   661 53711.33 22394.025
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2023.122121   4042.187107   -539.838711   2436.539874  -2973.125581 
##             7             8             9            10            11 
##    517.476134  -5657.715493  -1185.589480  -3963.685033   -413.203966 
##            12            13            14            15            16 
##  -4935.619269  -1602.455825   -892.826655    383.852333  -3237.935154 
##            17            18            19            20            21 
##   -371.479808  -2124.552244   6610.249493  -1529.397968  -1207.720870 
##            22            23            24            25            26 
##   1476.626639  -1187.256148    234.578692   1694.377469  -7104.256425 
##            27            28            29            30            31 
##    950.754768   8194.212156    413.336096    -18.817655  -2405.047750 
##            32            33            34            35            36 
##   1573.860291   4568.999750   1120.136306   2384.429159  -1876.178482 
##            37            38            39            40            41 
##   4601.964129   4300.240249  -2281.753682  -2984.985304  -1110.983206 
##            42            43            44            45            46 
## -10741.354235   7297.590289   2558.844930   1366.250970   8103.577011 
##            47            48            49            50            51 
##    678.574359   6521.606779   6703.167405  -5897.258899  -4803.991705 
##            52            53            54            55            56 
##  -5063.544250  -7928.044099   6136.308175  -4075.644717  -4890.402453 
##            57            58            59            60            61 
##   3862.932251    891.062174    -30.020595    144.035839  -4995.001160 
##            62            63            64            65            66 
##  18131.810578   3631.185200  -3657.163840   5918.521708   7333.600637 
##            67            68            69            70            71 
##  14624.252518   1669.380835 -13234.688513  -1315.059115   4636.831454 
##            72            73            74            75            76 
##  -4909.601494  -4408.805259 -10497.655906   2475.074108  -5394.297670 
##            77            78            79            80            81 
##   1072.948366  -6858.939307    558.881100  -2344.429838  -2683.012816 
##            82            83            84            85            86 
##  -3920.127804   -523.994834   2326.965954   3771.662064    481.706731 
##            87            88            89            90            91 
##   -480.795706    200.215039   4304.873397  -1163.940060   1150.931498 
##            92            93            94            95            96 
##  -2065.352571  -1043.435821    179.148275    275.985331  -7483.232495 
##            97            98            99           100           101 
##   2398.556199  -8598.431557  -2929.860476  -4027.460048  -1722.660649 
##           102           103           104           105           106 
##  -1247.353033   3194.438577  -2332.778831   2604.112338  -1151.679724 
##           107           108           109           110           111 
##    977.319145   2592.308562  -3151.953005  -4718.315767   -842.186118 
##           112           113           114           115           116 
##   1911.343164  11698.857370  -1247.554806   2665.220796   4257.797535 
##           117           118           119           120           121 
##   3494.838294  -1109.587555  -4723.779103  -3726.651484   2320.684220 
##           122           123           124           125           126 
##  -1733.669135   1341.036823   8857.759762    839.495699    123.181067 
##           127           128           129           130           131 
##  -2527.651505   2651.623263   7047.407915   1002.267384  -8509.062852 
##           132           133           134           135           136 
##   1747.756681   4132.854739  -3169.629232  -1422.067283   -854.815123 
##           137           138           139           140           141 
##  -3879.994737   1186.290943   -493.564441  -2911.485043   1722.447496 
##           142           143           144           145           146 
##  -1878.716717  -7825.665892   2049.097725  -3472.954797   2110.984609 
##           147           148           149           150           151 
##   -251.590908   1028.337982   -355.548795   1355.726017   1188.563121 
##           152           153           154           155           156 
##   3357.248080  -4864.000172  -1172.414512  -3233.053231   5961.787184 
##           157           158           159           160           161 
##   9746.145971  -3634.863739  -4979.511130   3405.586642     -8.323599 
##           162           163           164           165           166 
##   2491.693491  -6118.621705  -6951.073585   3957.834770  17187.616718 
##           167           168           169           170           171 
##   3398.045380   -632.180889  -2678.077893  -1332.372974   3364.027201 
##           172           173           174           175           176 
##   -458.952221  -8305.300910   2644.023772   4101.399980    395.689717 
##           177           178           179           180           181 
##   8520.026726  -9489.690090  -3699.814481 -10968.409600 -11451.134243 
##           182           183           184           185           186 
##   1035.723522   9088.948081  -1649.416754   5709.509780   6326.000960 
##           187           188           189           190           191 
##  12917.670547   8169.116094  -4337.966828   2196.372366  10096.277066 
##           192           193           194           195           196 
##  -1931.839946  -2728.219052 -10558.503578  -6623.241145    984.242458 
##           197           198           199           200           201 
##  -5484.626539 -10038.594405   5156.605769  -3305.021297  -1944.712957 
##           202           203           204           205           206 
##  -1034.672380   6263.694257   9637.208301    313.285022   2658.554142 
##           207           208           209           210           211 
##   2827.100225   5509.170171  12549.998664  -5990.926090 -11584.495074 
##           212           213           214           215           216 
##  -5930.758140 -10839.793278  -5307.865693   1302.360351 -13239.192106 
##           217           218           219           220           221 
##  16182.636684   7568.979324   1271.809421  26428.965537  12215.855108 
##           222           223           224           225           226 
##   7004.974488  13689.336857  -4269.859431  -2080.022829   3450.197128 
##           227           228           229           230           231 
##     33.910011   2428.110716   8690.196177   5509.199697  -2226.570141 
##           232           233           234           235           236 
##  -2135.197758   9129.473607 -11815.538989  -7563.286968  -8804.500883 
##           237           238           239           240           241 
## -10347.894617   2849.190683   1116.530968  -8535.935160  -9213.897424 
##           242           243           244           245           246 
##   8884.703127  -7995.834669   2269.474394 -10526.670615  -4262.679503 
##           247           248           249           250           251 
##   1217.299099    790.194088 -12534.804544   3444.176214   1850.553911 
##           252           253           254           255           256 
##   3991.136810   1901.837388  -1400.599551  10899.146295  20615.259308 
##           257           258           259           260           261 
##   2885.327372  -4586.930818   3806.725212  -2003.029733   3436.146932 
##           262           263           264           265           266 
##  -5157.444807 -11185.203482  -4994.517065   -777.387958  -5443.762096 
##           267           268           269           270           271 
##   8532.472608  -4548.523024   3929.729721  -2377.985156   4163.704050 
##           272           273           274           275           276 
##    430.312147   7022.225294  -1710.577126  11730.886581  -4908.127886 
##           277           278           279           280           281 
##   1414.810375   -685.331352   7541.582941  -5385.051956  -3041.429486 
##           282           283           284           285           286 
## -11560.872994  -2935.906328  18395.421344   7472.893211   2404.863144 
##           287           288           289           290           291 
##   -961.228447    579.570568   6073.302689   6543.667920 -19124.988049 
##           292           293           294           295           296 
## -11429.097413  -8374.947666   9436.367002   2813.560254  -1446.454886 
##           297           298           299           300           301 
##  27138.806534   9719.204956   4530.925109   9142.462529   2462.305291 
##           302           303           304           305           306 
##  -1423.095207   7521.211793 -24683.795770  -3832.351597   -455.532580 
##           307           308           309           310           311 
##  -7243.491863  -4220.593714   2698.187119  -9435.225956  -3441.289314 
##           312           313           314           315           316 
##  -8387.410871   1389.908630  -3336.355010   1869.292564  -4274.093733 
##           317           318           319           320           321 
##  27262.342151  -1026.467741   2993.549326  10523.712554   5248.762832 
##           322           323           324           325           326 
##  32028.254848   4658.581674 -21385.116731   1437.835469    761.014380 
##           327           328           329           330           331 
##  -6807.460639  -2043.711086 -33563.592806    745.755872  -2443.138796 
##           332           333           334           335           336 
##   -227.485297  -3304.808006   3956.872512   -585.859106  -7103.056778 
##           337           338           339           340           341 
##  -3244.138388  -2312.601577  -7797.993477   3756.027071  -1490.836818 
##           342           343           344           345           346 
##  -1859.194081  -1115.386391     52.169212    349.777334  -1758.724018 
##           347           348           349           350           351 
##  -9586.652357 -13320.118237   2241.856952  -4413.199323  -3742.100372 
##           352           353           354           355           356 
##  -6060.223282   1682.393458   1295.496997   2646.327499  -3895.893778 
##           357           358           359           360           361 
##   -639.959127    546.208426   6870.843317     96.812683   -223.593147 
##           362           363           364           365           366 
##   2394.207956  -2952.838793  -1069.029323  -8932.322018  -4777.488263 
##           367           368           369           370           371 
##  -6347.248624  -5062.376585  -7351.190005   4938.720014    259.857463 
##           372           373           374           375           376 
##   6997.091999  -7799.138270  -2396.215631  -3517.132198  -2587.494033 
##           377           378           379           380           381 
## -12573.980888   1833.301916 -10724.611447   5641.415142   9240.810726 
##           382           383           384           385           386 
##   2976.580564  -2569.830932   1438.925750   6565.354605  11196.103825 
##           387           388           389           390           391 
##  -6072.491227  -5607.985211   -379.938093   8339.552255   1549.997049 
##           392           393           394           395           396 
##  10949.543794 -10201.277878   2500.246350    427.849473    277.427955 
##           397           398           399           400           401 
##   -937.937102   -840.677785 -14758.996651   8327.374290  -1413.215251 
##           402           403           404           405           406 
##  -1595.824147   6767.230808  -8179.156251  -1500.938193  -2726.806175 
##           407           408           409           410           411 
##  -5999.972981  -3010.405390  -4056.087350  -8878.403012   6047.375900 
##           412           413           414           415           416 
##   1514.765205  -7514.224980  -7802.750738  14139.848993   3651.054949 
##           417           418           419           420           421 
##   4299.603025  -8255.723242  -4924.465344  -2758.887484   2672.506796 
##           422           423           424           425           426 
## -14176.020621  -2890.631318  -9191.926469   2955.619733   6892.061936 
##           427           428           429           430           431 
##   6444.022063  -4159.617934  -4276.724732  -4861.736696  -1911.060441 
##           432           433           434           435           436 
##  -5831.251435  -6725.357065  -6025.323108  -1451.603174   -912.531790 
##           437           438           439           440           441 
##  -5048.521323   2520.083925   4751.803871  -5179.381712  -2264.226294 
##           442           443           444           445           446 
##   1471.861420  -3958.344275   2725.388507  -6710.429669 -12217.493798 
##           447           448           449           450           451 
##  -4569.336669   9595.948557  -2139.858554   4648.432915  -6005.598450 
##           452           453           454           455           456 
##  -1235.941445    269.101945   2903.121026 -12411.635373   3278.308917 
##           457           458           459           460           461 
##  -6816.531961   6430.997678   2882.095559   2357.610990  -4010.260817 
##           462           463           464           465           466 
##   1944.045484   -168.982486   1629.229278   -695.161451   3177.872258 
##           467           468           469           470           471 
##  -2828.181834   5629.489375  -7144.086007  -3130.487718  -2356.962682 
##           472           473           474           475           476 
##  -4805.749940   2874.171787   7657.288863  -6195.498045   1334.459809 
##           477           478           479           480           481 
##  -6336.646206  -2974.603983   1891.591007 -13063.510792  -9835.032438 
##           482           483           484           485           486 
##  -1248.429299    -32.981665  -1027.356415  -1413.500672  -9660.584852 
##           487           488           489           490           491 
##  11051.248829   6129.714737   7280.367779  -5612.852382   5215.227961 
##           492           493           494           495           496 
##   9115.058670   5835.005809 -13714.942601 -10740.288059  -3567.219448 
##           497           498           499           500           501 
##  -1219.694729   -637.591299  -7741.322121    525.215037   4192.390674 
##           502           503           504           505           506 
##   5388.458775    512.083376    -71.914671  -7392.617072    447.421506 
##           507           508           509           510           511 
##  -5176.680183   1723.262920  -1418.390098  -8277.801880   -690.775196 
##           512           513           514           515           516 
##  -2767.573961   -675.964788   1239.296916  -9600.637939  -7835.863233 
##           517           518           519           520           521 
##  24239.443749   9661.889334   5672.759775  -5564.364007   2598.339160 
##           522           523           524           525           526 
##  16809.880113  11193.961074 -24468.249641  -5259.623211  -3907.492442 
##           527           528           529           530           531 
##   4415.334423   -534.298224 -11277.725040   4265.620287  13761.090761 
##           532           533           534           535           536 
##  -5187.762298   4187.825295   5351.055881  -2015.870190  -4753.478369 
##           537           538           539           540           541 
##  -7262.278405  -2255.057262   8177.024797    -55.772463  -8323.022767 
##           542           543           544           545           546 
##   1671.845670   -753.000604    214.835489 -11184.722952 -11168.284379 
##           547           548           549           550           551 
##   1976.629389   6921.087875  -1438.096416    718.048319  -7847.395853 
##           552           553           554           555           556 
##   8468.132457    766.459062 -12090.864995   9065.020554   8513.270891 
##           557           558           559           560           561 
##    -86.344102   4668.078928  -3779.693653  13921.501958  21251.029249 
##           562           563           564           565           566 
##  -6801.982582  -9972.766349   6536.935165    -34.930183   3201.440925 
##           567           568           569           570           571 
##  -7637.101790 -17525.463113   6528.679775   6271.322045   1712.864239 
##           572           573           574           575           576 
##   2904.623429   1567.231243  -2370.186629  14526.522710  -9900.330032 
##           577           578           579           580           581 
##  -6446.244138   8540.553572   2651.290368  -6760.276442   7327.964788 
##           582           583           584           585           586 
##  -4010.623150  -2964.563361  15529.110736 -14738.654412   8258.251087 
##           587           588           589           590           591 
##   -134.087180  -6411.924340   -921.805523     89.895345 -10814.628676 
##           592           593           594           595           596 
##   1682.713746  -7269.010775   2971.604465   8752.414440  -7655.830795 
##           597           598           599           600           601 
##   5728.316135   2584.088451   6693.076383  -3380.983068   5976.184430 
##           602           603           604           605           606 
##  -8495.350384   2098.537755   1104.936385   2969.931313   1316.067839 
##           607           608           609           610           611 
##    215.600681  -5990.025593   7923.574921  -1367.474315  -2748.184446 
##           612           613           614           615           616 
##  -3612.523393  -8370.280709  11851.868397   4777.591436  -9491.324310 
##           617           618           619           620           621 
##  11492.112665   5868.055015  -5774.589797  26188.607558 -13096.912776 
##           622           623           624           625           626 
##  -6982.442417   2990.487507  -4334.185946 -10745.382767  11188.367334 
##           627           628           629           630           631 
## -21790.290997  -2482.786487   8610.439057  11030.433195  -1703.482606 
##           632           633           634           635           636 
##  33141.729172  -6856.688759   5488.372143   5159.230270  -2517.064878 
##           637           638           639           640           641 
##  -5571.531606  -2134.773082 -12610.686289  -2368.750309  -2003.934886 
##           642           643           644           645           646 
##  -2631.590629  -2961.136871   1720.453447   4326.070101  16840.772154 
##           647           648           649           650           651 
##  18363.717034    628.926605   4543.634655  10360.059434  19872.033497 
##           652           653           654           655           656 
##    411.071340 -28374.042188  -1525.090726  -2460.241276   1716.215460 
##           657           658           659           660           661 
##  -3343.753519 -10755.267801   1573.033520   4128.524246  -1119.206262 
##           662           663           664           665           666 
##  12924.713844   1208.941106   1662.817029 -11842.486904   1272.328961 
##           667           668           669           670           671 
##   1077.021367  -5278.522339  -7503.225392   1998.450792  -3788.831993 
##           672           673           674           675           676 
##   2606.687886  -3457.168224  -9405.885783  -8349.986990  -3004.393819 
##           677           678           679           680           681 
##    144.962249   2809.502812    659.361327  -3888.076295  -1863.185333 
##           682           683           684           685           686 
##  -1373.085893  -8298.821275   4611.046744  -2301.264225  -1455.534140 
##           687           688           689           690           691 
##    529.130403  10788.668012   9749.313306  10495.971947  -9815.080801 
##           692           693           694           695           696 
##  -3669.063393  -3240.889149   5780.518732 -10491.762489  -7985.798457 
##           697           698           699           700           701 
##  -8664.975240  -6309.067354  -4764.229744   3061.187333  -4439.781405 
##           702           703           704           705           706 
##  -1931.278913   4186.832085  31053.904008   9409.848741  23330.446368 
##           707           708           709           710           711 
##   1548.821636   8204.844260  22806.032921   6435.733206 -18317.801599 
##           712           713           714           715           716 
##   4745.024396  -5517.843949   -162.394107    422.072935 -17321.619566 
##           717           718           719           720           721 
##  -5298.250621   3306.322005  -3046.210419 -13008.034566   4263.493960 
##           722           723           724           725           726 
##  -5579.394828    723.750022  -3956.319207 -12466.412474   1359.765742 
##           727           728           729           730           731 
##  -1879.944249  -9791.353666  17262.799403   1746.559358  -2754.294511 
##           732           733           734           735           736 
##   5686.109129  -8666.599841   -749.957930   8111.445976 -15388.225337 
##           737           738           739           740           741 
##  -5930.950519   7392.540646  -4810.817624    138.418894   1803.351438 
##           742           743           744           745           746 
##  -1983.724564  -5194.856323   6390.227190  -6306.050011  22673.824549 
##           747           748           749           750           751 
##   7777.098262  -2002.932936  -7339.161336  23375.438483  -4354.261495 
##           752           753           754           755           756 
##   1342.364319 -14474.172355  56074.294127  26837.909544  15000.490034 
##           757           758           759           760           761 
## -10740.498840  10534.495067   7241.986793   5739.555698 -46455.560507 
##           762           763           764           765           766 
## -16190.377632    961.240891  -2524.615819  -3463.725762 122839.885363 
##           767           768           769           770           771 
##  19222.211095  43630.674588  22479.827771  11969.952312  15746.931981 
##           772           773           774           775           776 
##  25629.534221 -98911.074682  -6783.211701 -35851.066289   1742.280646 
##           777           778           779           780           781 
##  -1226.473726   3397.934468  -7416.226050  -1442.351444  -1942.856554 
##           782           783           784           785           786 
##   3426.986415  -7155.803432  -2233.354556   3923.229504   2265.716590 
##           787           788           789           790           791 
##  -2760.481368  -4047.125602   1741.465509   2854.358702    -52.495250 
##           792           793           794           795           796 
##  -6704.391970  -5779.294276  -1141.179212  -1257.373839  -7811.804446 
##           797           798           799           800           801 
##  -2344.668502  -3259.079742  -2656.035699  10733.845810   2242.465103 
##           802           803           804           805           806 
##   7079.139928   2918.397072  -5454.553079   8185.499907   9867.779363 
##           807           808           809           810           811 
## -10614.320074  -7399.825202  -7503.056531   3012.814963   4198.455032 
##           812           813           814           815           816 
##  -2268.521790 -14162.327072  -4098.702891   6272.692275   8244.489970 
##           817           818 
##  -9671.986930  -7742.152242 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17246.16  20096.81  24355.98  24073.60  26429.84  23759.24  24476.43  19702.73 
##        10        11        12        13        14        15        16        17 
##  19438.97  16778.49  17556.90  14282.31  14333.54  14999.00  16697.65  15015.62 
##        18        19        20        21        22        23        24        25 
##  16051.55  15424.32  22515.40  21598.29  21077.52  22969.83  22294.99  22948.34 
##        26        27        28        29        30        31        32        33 
##  24796.54  18717.53  20445.79  28292.66  28350.39  28022.90  25649.43  27053.57 
##        34        35        36        37        38        39        40        41 
##  30901.29  31250.14  32661.04  30168.61  34142.76  37354.75  34407.27  31214.27 
##        42        43        44        45        46        47        48        49 
##  30060.64  20628.70  28156.58  30596.03  31686.57  38533.00  38026.96  42694.83 
##        50        51        52        53        54        55        56        57 
##  46936.26  39625.28  34187.12  29203.76  22339.83  28637.50  25213.97  21507.07 
##        58        59        60        61        62        63        64        65 
##  25920.79  27181.88  27479.25  27891.57  23757.48  40368.96  42215.16  37455.34 
##        66        67        68        69        70        71        72        73 
##  41667.40  46589.03  57270.19  55281.55  40506.77  38009.60  41031.17  35324.38 
##        74        75        76        77        78        79        80        81 
##  30771.08  21463.21  24668.58  20589.34  22677.94  17567.26  19585.14  18810.73 
##        82        83        84        85        86        87        88        89 
##  17837.27  15903.85  17183.18  20795.62  25218.72  26209.80  26234.78  26852.27 
##        90        91        92        93        94        95        96        97 
##  30982.37  29811.50  30812.07  28874.15  28072.99  28441.59  28848.66  22418.30 
##        98        99       100       101       102       103       104       105 
##  25437.00  18459.00  17313.75  15352.09  15652.21  16330.42  20808.49  19890.89 
##       106       107       108       109       110       111       112       113 
##  23406.25  23195.97  24874.12  27754.38  25249.46  21688.61  21964.37  24613.86 
##       114       115       116       117       118       119       120       121 
##  35491.55  33682.21  35521.92  38523.88  40482.16  38167.78  32982.51  29319.46 
##       122       123       124       125       126       127       128       129 
##  31404.81  29682.68  30865.67  38474.65  38116.68  37177.08  34036.81  35820.16 
##       130       131       132       133       134       135       136       137 
##  41224.59  40664.21  31855.24  33121.57  36315.20  32721.50  31106.82  30190.71 
##       138       139       140       141       142       143       144       145 
##  26743.57  28159.71  27929.06  25612.55  27639.43  26262.52  19856.90  22891.10 
##       146       147       148       149       150       151       152       153 
##  20715.16  23695.88  24236.52  25828.83  26011.13  27667.29  28969.61  32005.43 
##       154       155       156       157       158       159       160       161 
##  27470.13  26732.20  24284.50  30185.71  41655.29  39983.51  37345.27  42371.61 
##       162       163       164       165       166       167       168       169 
##  43781.88  47201.91  42662.36  37963.88  43395.67  59717.53  61932.32  60344.51 
##       170       171       172       173       174       175       176       177 
##  57166.37  55563.69  58269.52  57292.44  49575.26  52402.17  56149.31  56185.54 
##       178       179       180       181       182       183       184       185 
##  63322.98  53813.81  50560.84  41358.42  32887.56  36400.05  46515.70  45971.06 
##       186       187       188       189       190       191       192       193 
##  51931.00  57682.90  68478.88  73768.11  67455.20  67648.87  74727.70  70398.93 
##       194       195       196       197       198       199       200       201 
##  65916.36  55147.24  49170.19  50596.20  46185.59  38344.97  44777.45  43002.71 
##       202       203       204       205       206       207       208       209 
##  42640.24  43119.16  49921.36  58821.29  58450.45  60177.33  61835.12  65630.86 
##       210       211       212       213       214       215       216       217 
##  75108.78  67182.07  55356.90  49959.22  40944.72  37898.78  41016.19  31024.36 
##       218       219       220       221       222       223       224       225 
##  48018.31  55347.90  56250.89  79043.72  86547.74  88553.38  96153.86  87093.88 
##       226       227       228       229       230       231       232       233 
##  81085.09  80666.52  77312.46  76472.95  81215.66  82581.57  77010.34  72217.53 
##       234       235       236       237       238       239       240       241 
##  77877.97  64509.72  56536.64  48477.61  40079.10  44276.04  46431.36  39874.18 
##       242       243       244       245       246       247       248       249 
##  33546.15  43840.98  38080.95  42021.38  34275.97  32980.27  36639.95  39467.23 
##       250       251       252       253       254       255       256       257 
##  30285.68  36230.87  40036.86  45237.88  47959.46  47451.43  57764.74  75282.96 
##       258       259       260       261       262       263       264       265 
##  75097.79  68400.42  69884.03  66100.28  67548.16  61298.35  50560.09  46582.67 
##       266       267       268       269       270       271       272       273 
##  46792.33  42894.38  51709.09  47977.70  52129.41  50243.72  54315.97  54612.35 
##       274       275       276       277       278       279       280       281 
##  60637.01  58268.40  67952.99  61870.48  62080.76  60427.85  66177.62  59900.57 
##       282       283       284       285       286       287       288       289 
##  56460.30  46000.05  44394.86  61647.82  67184.57  67594.51  65009.00  64095.27 
##       290       291       292       293       294       295       296       297 
##  68101.05  72015.99  52989.67  43079.80  37083.63  47417.44  50663.17  49776.05 
##       298       299       300       301       302       303       304       305 
##  74001.51  79954.07  80622.54  85240.55  83436.95  78461.22  81932.22  56800.78 
##       306       307       308       309       310       311       312       313 
##  53057.39  52736.78  46519.45  43725.53  47333.23  39876.43  38596.98  33151.95 
##       314       315       316       317       318       319       320       321 
##  36941.07  36121.42  39957.52  37939.51  63757.04  61595.59  63221.14  71228.95 
##       322       323       324       325       326       327       328       329 
##  73619.17  99131.70  97507.40  73308.31  72104.70  70460.03  62402.00  59520.74 
##       330       331       332       333       334       335       336       337 
##  29432.67  33124.71  33564.77  35887.52  35227.56  41001.57  42078.49  37320.28 
##       338       339       340       341       342       343       344       345 
##  36533.74  36660.56  31973.83  37980.12  38644.34  38903.10  39779.97  41568.08 
##       346       347       348       349       350       351       352       353 
##  43392.30  43143.65  36079.69  26636.00  31987.20  30846.81  30436.37  28049.89 
##       354       355       356       357       358       359       360       361 
##  32734.50  36493.39  40962.47  39149.24  40411.08  42552.16  49956.47  50507.74 
##       362       363       364       365       366       367       368       369 
##  50709.65  53175.84  50656.17  50100.04  42736.20  39929.53  36101.81  33877.76 
##       370       371       372       373       374       375       376       377 
##  29930.71  37227.57  39517.34  47412.57  41376.79  40823.28  39358.78  38890.98 
##       378       379       380       381       382       383       384       385 
##  29747.41  34351.18  27394.30  35623.76  45969.56  49539.40  47810.65  49804.79 
##       386       387       388       389       390       391       392       393 
##  56032.61  65529.78  58732.70  53194.08  52922.45  60311.15  60835.17  69514.56 
##       394       395       396       397       398       399       400       401 
##  58606.75  60175.58  59735.14  59218.37  57703.39  56463.43  43205.63  51801.93 
##       402       403       404       405       406       407       408       409 
##  50801.11  49766.05  56175.30  48708.51  48018.81  46343.40  42015.26  40844.52 
##       410       411       412       413       414       415       416       417 
##  38905.97  32992.77  40875.38  43805.37  38471.04  33553.15  48443.37  52292.97 
##       418       419       420       421       422       423       424       425 
##  56227.15  48686.89  45005.60  43679.92  47270.88  35675.49  35404.36  29655.95 
##       426       427       428       429       430       431       432       433 
##  35252.80  43590.84  50491.62  47253.01  44318.02  41239.35  41127.39  37600.79 
##       434       435       436       437       438       439       440       441 
##  33734.32  30964.89  32542.96  34394.66  32396.77  37269.05  43482.38  40230.65 
##       442       443       444       445       446       447       448       449 
##  39936.28  42946.49  40829.90  44824.43  40065.35  31086.34  29922.34  41293.57 
##       450       451       452       453       454       455       456       457 
##  40974.71  46633.03  42263.66  42613.76  44236.31  47959.21  37820.69  42676.10 
##       458       459       460       461       462       463       464       465 
##  38093.57  45672.19  49196.67  51820.55  48545.95  50889.70  51091.49  52840.73 
##       466       467       468       469       470       471       472       473 
##  52337.70  55285.18  52610.08  57667.66  50919.06  48526.96  47111.32  43731.40 
##       474       475       476       477       478       479       480       481 
##  47492.28  54965.07  49384.97  51090.36  45872.60  44249.55  47086.08  36486.89 
##       482       483       484       485       486       487       488       489 
##  30040.29  31911.98  34612.07  36103.93  37071.01  30703.75  43249.86  49918.49 
##       490       491       492       493       494       495       496       497 
##  56757.42  51462.20  56301.37  63944.71  67760.94  53999.86  44565.79  42588.27 
##       498       499       500       501       502       503       504       505 
##  42911.88  43704.04  38183.78  40585.75  45893.97  51582.77  52293.34  52404.05 
##       506       507       508       509       510       511       512       513 
##  46098.01  47439.68  43694.17  46453.10  46118.37  39826.20  40958.72  40132.82 
##       514       515       516       517       518       519       520       521 
##  41239.85  43883.21  36714.29  31987.70  55907.54  64078.53  67736.08  61106.80 
##       522       523       524       525       526       527       528       529 
##  62447.98  76050.75  83036.25  57954.91  52818.49  49508.67  53893.16  53398.87 
##       530       531       532       533       534       535       536       537 
##  43570.09  48568.19  61244.62  55758.60  59160.52  63153.30  60202.19  55226.71 
##       538       539       540       541       542       543       544       545 
##  48680.77  47334.98  55282.06  55032.17  47582.87  49809.29  49635.74  50330.44 
##       546       547       548       549       550       551       552       553 
##  40967.71  32793.23  37140.48  45267.24  45063.95  46771.97  40774.30  49798.54 
##       554       555       556       557       558       559       560       561 
##  50955.29  40721.69  50274.59  58147.20  57511.35  61113.55  56875.50  68650.69 
##       562       563       564       565       566       567       568       569 
##  85360.13  75438.77  63988.06  68412.79  66534.84  67722.96  59282.46  43251.61 
##       570       571       572       573       574       575       576       577 
##  50268.96  56181.42  57365.66  59443.77  60091.62  57214.48  69476.33  58836.53 
##       578       579       580       581       582       583       584       585 
##  52551.73  60162.71  61668.56  54754.04  61028.34  56598.99  53639.89  67226.80 
##       586       587       588       589       590       591       592       593 
##  52637.32  59990.66  59081.92  52796.38  52100.68  52377.06  43081.43  45881.73 
##       594       595       596       597       598       599       600       601 
##  40501.54  44752.59  53526.69  46849.68  52715.91  55096.64  60772.70  56926.10 
##       602       603       604       605       606       607       608       609 
##  61745.78  53304.03  55186.35  55963.64  58274.65  58849.40  58389.60  52559.85 
##       610       611       612       613       614       615       616       617 
##  59630.19  57687.90  54781.52  51483.57  44437.85  55962.27  59854.47  50778.74 
##       618       619       620       621       622       623       624       625 
##  61193.52  65383.59  58865.39  81120.20  66224.73  58544.66  60550.04  55897.67 
##       626       627       628       629       630       631       632       633 
##  46221.20  56941.72  37474.22  37334.28  46914.28  57409.77  55451.99  84216.12 
##       634       635       636       637       638       639       640       641 
##  74390.34  76593.77  78233.06  72952.96  65663.34  62293.54  50183.75  48550.08 
##       642       643       644       645       646       647       648       649 
##  47440.30  45920.71  44303.40  46983.50  51606.51  66595.57  81037.36  78157.22 
##       650       651       652       653       654       655       656       657 
##  79062.08  84940.68  98401.64  93153.90  63387.95  60836.67  57787.36  58773.18 
##       658       659       660       661       662       663       664       665 
##  55209.84  45610.97  47998.19  52321.21  51512.43  63088.20  62965.75  63255.63 
##       666       667       668       669       670       671       672       673 
##  51697.10  53058.26  54077.95  49411.08  43383.55  46422.12  44018.03  47509.03 
##       674       675       676       677       678       679       680       681 
##  45258.74  38087.70  32739.25  32736.75  35489.07  40226.78  42489.93  40492.04 
##       682       683       684       685       686       687       688       689 
##  40515.66  40964.96  35300.52  41637.55  41134.39  41434.01  43431.90  54152.54 
##       690       691       692       693       694       695       696       697 
##  62620.03  70678.94  59962.92  55965.89  52844.48  58004.76  48285.94  41977.40 
##       698       699       700       701       702       703       704       705 
##  35865.78  32580.94  31059.10  36572.35  34833.85  35507.31  41447.38  70141.29 
##       706       707       708       709       710       711       712       713 
##  76307.27  93875.46  90190.30  92788.68 107831.84 106671.09  84005.83  84353.56 
##       714       715       716       717       718       719       720       721 
##  75681.54  72780.78  70754.91  53463.96  48856.82  52353.07  49854.89  38957.08 
##       722       723       724       725       726       727       728       729 
##  44531.68  40798.54  43046.32  40918.98  31615.23  35570.66  36196.64  29824.63 
##       730       731       732       733       734       735       736       737 
##  47913.73  50164.01  48195.61  53856.17  46253.82  46528.70  54519.51  40955.09 
##       738       739       740       741       742       743       744       745 
##  37362.89  45874.10  42644.87  44149.22  46921.15  46033.28  42448.20  49445.19 
##       746       747       748       749       750       751       752       753 
##  44460.46  65447.19  70773.65  66878.45  58804.42  78606.40  71672.64  70590.60 
##       754       755       756       757       758       759       760       761 
##  55810.71 104587.23 121677.51 126271.78 107776.36 110207.44 109454.02 107480.99 
##       762       763       764       765       766       767       768       769 
##  60104.23  45138.04  47049.47  45672.44  43646.69 152343.07 156785.04 182018.32 
##       770       771       772       773       774       775       776       777 
## 185588.90 179519.64 177514.75 184404.79  81504.78  72083.21  38419.43  41856.33 
##       778       779       780       781       782       783       784       785 
##  42265.78  46668.51  41060.92  41381.29  41223.73  45782.52  40513.78  40210.91 
##       786       787       788       789       790       791       792       793 
##  45330.71  48358.91  46611.41  43957.68  46699.50  50070.92  50477.25  45014.72 
##       794       795       796       797       798       799       800       801 
##  41046.18  41631.80  42042.38  36668.81  36750.65  36022.46  35913.01  47528.39 
##       802       803       804       805       806       807       808       809 
##  50260.72  56880.75  59031.70  53589.79  60760.08  68502.75  57360.54  50426.77 
##       810       811       812       813       814       815       816       817 
##  44272.04  48086.40  52459.52  50628.18  38623.85  36926.45  44512.94  52872.84 
##       818 
##  44514.44 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.811
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    4.931073   0.7705721    3.888013
## t2* 2636.027643 166.0158776  873.069298
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.055872       4.851715   13.10195
## 2    lag_depvar 1596.648803    2686.238082 4456.17183

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Sat Mar  1 16:58:26 2025
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#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 0.0000 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 258.8850 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 59.9440 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 0.0000 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.0000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 0.0000 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 36.9995 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 10.9950 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.0000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.0000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 366.8235 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2624, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2624 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.2
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.2
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.2
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-03-09 00:04:58 sería de: 26.921 pesos// Percentil 95% más alto proyectado: 35.097,94

Según TimeGPT: La proyección de la UF a 298 días más 2026-01-01 sería de: 39.556,3 pesos// Percentil 80% más alto proyectado: 39.946,4 pesos// Percentil 95% más alto proyectado: 41.030,12

Según prophet: La proyección de la UF a 298 días más 2026-01-01 sería de: 38.446 pesos// Percentil 95% más alto proyectado: 40.942

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26537.82 26331.66
Lo.80 26669.99 26493.43
Point.Forecast 26921.46 26799.01
Hi.80 31593.20 32074.20
Hi.95 34386.03 34866.72


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,2) 
## 
## Coefficients:
##           ma1      ma2
##       -0.5565  -0.3087
## s.e.   0.1110   0.1126
## 
## sigma^2 = 37449:  log likelihood = -474.19
## AIC=954.39   AICc=954.75   BIC=961.18
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1  intercept     xreg
##       0.3642   463.3959  18.3018
## s.e.  0.1004   275.1981   8.4991
## 
## sigma^2 = 35487:  log likelihood = -477.87
## AIC=963.74   AICc=964.34   BIC=972.85
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 700.1710 706.9536 742.9974
Lo.80 836.1806 850.5687 836.9886
Point.Forecast 1093.1089 1121.8640 1059.2415
Hi.80 1350.0372 1413.0247 1339.9033
Hi.95 1486.0468 1567.1559 1517.1589


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.5         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.12     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.23.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.1       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.8.6      httr_1.4.7         
## [40] readxl_1.4.4        zoo_1.8-13          stringr_1.5.1      
## [43] stringi_1.8.4       DataExplorer_0.8.3  data.table_1.16.4  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.0.0   
##   [4] httr2_1.1.0         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.16.3      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.0.2       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.9          rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.1       askpass_1.2.1       pkgbuild_1.4.6     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       tokenizers_0.3.0    listenv_0.9.1      
##  [31] anytime_0.3.11      performance_0.13.0  spatial_7.3-17     
##  [34] parallelly_1.42.0   codetools_0.2-20    xml2_1.3.6         
##  [37] tidyselect_1.2.1    ggeffects_2.2.0     farver_2.1.2       
##  [40] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [43] stats4_4.4.0        jsonlite_1.8.9      ellipsis_0.3.2     
##  [46] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.1  
##  [49] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [52] xfun_0.50           TTR_0.24.4          ggfortify_0.4.17   
##  [55] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [58] fastmap_1.2.0       boot_1.3-30         openssl_2.3.2      
##  [61] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [64] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [67] networkD3_0.4       gtools_3.9.5        generics_0.1.3     
##  [70] htmlwidgets_1.6.4   ggstats_0.8.0       pkgconfig_2.0.3    
##  [73] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [76] selectr_0.4-2       janeaustenr_1.0.0   htmltools_0.5.8.1  
##  [79] carData_3.0-5       tseries_0.10-58     snakecase_0.11.1   
##  [82] knitr_1.49          rstudioapi_0.17.1   tzdb_0.4.0         
##  [85] uuid_1.2-1          nlme_3.1-164        curl_6.2.0         
##  [88] cachem_1.1.0        sjlabelled_1.2.0    KernSmooth_2.23-22 
##  [91] parallel_4.4.0      fBasics_4041.97     pillar_1.10.1      
##  [94] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [97] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
## [100] evaluate_1.0.3      mvtnorm_1.3-3       cli_3.6.4          
## [103] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [106] future.apply_1.11.3 labeling_0.4.3      sjmisc_2.8.10      
## [109] rstan_2.32.6        QuickJSR_1.5.1      viridisLite_0.4.2  
## [112] assertthat_0.2.1    munsell_0.5.1       lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.34.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.26     bit_4.5.0.1
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))